Method for quantification of uncertainty of contours in manual &amp; auto segmenting algorithms

ABSTRACT

A system ( 10 ) quantifies uncertainty in contours. The system ( 10 ) includes at least one processor ( 42 ) programmed to receive an image ( 18 ) including an object of interest (OOI) ( 20 ). Further, a band of uncertainty ( 32 ) delineating a region ( 34 ) in the received image ( 18 ) is received. The region ( 34 ) includes the boundary of the OOI ( 20 ). The boundary is delineated in the region ( 34 ) using iterative filtering of the region ( 34 ) and a metric of uncertainty of the delineation is determined for the region ( 34 ).

This application is a Continuation of U.S. application Ser. No.14/375,836, filed Jul. 31, 2014, which is the U.S. National Phaseapplication under 35 U.S.C. § 371 of International Application No.PCT/IB2013/050952, filed on Feb. 5, 2013, which claims the benefit ofU.S. Provisional Patent Application No. 61/598,368, filed on Feb. 14,2012. These applications are hereby incorporated by reference herein.

The present application relates generally to image processing. It findsparticular application in conjunction with segmenting medical images andwill be described with particular reference thereto. However, it is tobe understood that it also finds application in other usage scenariosand is not necessarily limited to the aforementioned application.

The grade and intensity of a lesion, such as a tumor, is an importantfactor in determining a diagnosis and available treatment options forthe patient. Typically, the grade and intensity of a lesion isdetermined by assessing images of the lesion. Nuclear medical imagingmodalities are the primary imaging modality for generating the images.In assessing the images, lesion delineation is an important step forcorrectly determining the grade and intensity of the lesion. However,lesion delineation can be challenging.

Malignant tumors are characterized by fuzzy and irregular boundaries.Hence, detection of lesion boundaries is a difficult problem and oftenrequires the manual intervention of skilled physicians. In some cases,even skilled physicians are unable to determine the boundary withconfidence. Filtering methods exist to enhance an image, but methods offiltering are based on correcting the image for errors such as scatter,attenuation, and so on. When the anatomy is inherently fuzzy, theresultant filtered image continues to be difficult to outline anddelineate. Hence, there may be a low confidence level in lesiondelineation.

A low confidence in the lesion boundary is not necessarily adisadvantage. Physicians use the irregular and imprecise nature of theboundary as an important characteristic of tumors which distinguishestumors from benign lesions. For example, tumor boundary irregularity canbe used to distinguish between active tuberculosis nodules and malignantlesions (both of which have high metabolism and take upfluorodeoxyglucose (FDG) in positron emission tomography (PET)preferentially). Hence, it is important to characterize, preferablyquantitatively, the uncertainty in delineation of the boundary foraiding diagnosis and indicating the confidence in the results.

Current methods of region demarcation result in a binary output whichindicates the decision made by a user or an algorithm. Further, even ifthe boundary is to be further modified, it can only be done so on thebasis of a new evaluation. The uncertainty of the previously identifiedboundary is not used to guide the next stage or assess confidence infinal results. Further, there is no way for obtaining uncertaintyinformation from automatic algorithms.

The present application provides new and improved methods and systemswhich overcome the above-referenced challenges and others.

In accordance with one aspect, a system for quantification ofuncertainty of contours is provided. The system includes at least oneprocessor programmed to receive an image including an object of interest(OOI). Further, a band of uncertainty delineating a region in thereceived image is received. The region includes the boundary of the OOI.The boundary in the region is delineated using iterative filtering ofthe region and at least one metric of uncertainty of the delineation forthe region is determined.

In accordance with another aspect, a method for quantification ofuncertainty of contours is provided. The method includes receiving animage including an object of interest (OOI). Further, a band ofuncertainty delineating a region in the received image is received. Theregion includes the boundary of the OOI. The boundary is delineated inthe region using iterative filtering of the region and at least onemetric of uncertainty of the delineation is determined for the region.

In accordance with another aspect, a system for quantification ofuncertainty of contours is provided. The system includes a processorprogrammed to receive an image including an object of interest (OOI).Further, a band of uncertainty delineating a region in the receivedimage is received. The region includes the boundary of the OOI. For eachof a plurality of sub-regions of the region, a determination as towhether to filter the sub-region is made. The determination including atleast one of determining whether the boundary of the OOI can bedelineated in the sub-region with a confidence level exceeding a firstpredetermined level and determining whether a stopping condition is met.The stopping condition indicates the confidence level is less than asecond predetermined level. In response to determining the the boundaryof the OOI can be delineated in the sub-region with the confidence levelexceeding the first predetermined level or the stopping condition beingmet, the boundary of the OOI is delineated in the sub-region. Inresponse to determining the the boundary of the OOI cannot be delineatedin the sub-region with the confidence level exceeding the firstpredetermined level and the stopping condition not being met, thesub-region is iteratively filtered a predetermined number of times andthe determination as to whether to filter the sub-region is repeated.

One advantage resides in separate identification and quantification ofareas of certainty and areas of uncertainty.

Another advantage resides in filtering sub-regions to a different extentbased on a confidence level.

Another advantage resides in providing experts additional information toaid in segmentation of areas of uncertainty.

Another advantage resides in less time for accurate delineation in areasof irregular, fuzzy boundaries.

Another advantage resides in indicating accuracy of a part of or theentire identified boundary.

Still further advantages of the present invention will be appreciated tothose of ordinary skill in the art upon reading and understand thefollowing detailed description.

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 illustrates a block diagram of a system for quantifyinguncertainty of contours in manual and auto segmenting algorithms.

FIG. 2 illustrates a positron emission tomography (PET) image of apatient with malignant lesions in the lung.

FIG. 3 illustrates a zoomed axial view of the PET image of FIG. 2.

FIGS. 4 and 5 illustrate a block diagram of a method for delineating anobject of interest in a image.

FIG. 6A-D illustrate an image at increasing degrees of filtering.

FIG. 7 illustrates a portion of a band of uncertainty with an innersegment and an outer segment of a point of uncertainty.

With reference to FIG. 1, a therapy system 10 includes one or moreimaging modalities 12 for acquiring images of objects of interest, suchas legions, within patients. The imaging modalities 12 suitably includeone or more of a computed tomography (CT) scanner, a positron emissiontomography (PET) scanner, a magnetic resonance (MR) scanner, a singlephoton emission computed tomography (SPECT) scanner, a cone-beamcomputed tomography (CBCT) scanner, and the like. Images acquired fromthe imaging modalities 12 are stored in one or more image memories 14.

A segmentation device 16 receives an image 18, such as a three- and/orfour-dimensional image, of an object of interest (OOI) 20, such as alesion, an example of which is shown in FIGS. 2 and 3. The receivedimage 18 can, for example, be a Dynamic Contrast Enhanced MR image.Typically, the image 18 is received from the imaging modalities 12and/or the image memories 14. For example, the image 18 can be receivedfrom the imaging modalities 12 through the image memories 14. However,other sources for the image 18 are contemplated. Further, the image 18is typically received from nuclear imaging modalities. Through executionof a segmentation application 22 of the segmentation device 16, thesegmentation device 16 delineates the OOI 20 in the received image 18.If the received image 18 is four-dimensional, the OOI 20 is delineatedin all phases of the received image 18.

When the segmentation application 22 is executed, a user interfacethereof is displayed on a display device 24 of the segmentation device16. The user interface suitably allows an associated user to view thereceived image 18. Further, the user interface allows the associateduser to create and/or modify contours 26, 28 on the received imaged 18using a user input device 30 of the of the segmentation device 16. Acontour specifies the boundary of a region, such as a lesion, in atwo-dimensional image space. Hence, the associated user can, forexample, employ a mouse to draw a contour on the received image 18and/or resize a contour on the received image 18. In some embodiments,the user interface further allows the associated user to specifyparameters for segmentation using the user input device 30.

To delineate the OOI 20 in the received image 18, the segmentationapplication 22 employs a method 50 of FIG. 4. According to the method50, a band of uncertainty 32, an example of which is illustrated inFIGS. 2 and 3, is received 52 for the received image 18. The band ofuncertainty 32 is defined by an outer contour 26 and an inner contour28, which collectively identify a region 34 within which the boundary ofthe OOI 20 is expected. The region 34 typically includes a portion ofthe received image 18, but can also include the entire image. The outercontour 26 marks a region within which the boundary of the OOI 20 is,and the inner contour 28 marks a region outside of which the boundary ofthe OOI 20 is.

Typically, the associated user draws the band of uncertainty 32 usingthe user interface such that the inner contour 28 and the outer contour26 are received from the user input device 30. However, the band ofuncertainty 32 can be received from other sources. For example, the bandof uncertainty 32 can be received from an algorithm for automaticallydetermining the band of uncertainty 32.

After receiving the band of uncertainty 32, for the region 34 or each ofa plurality of sub-regions of the region 34, the boundary is delineated54 in the region 34 or the sub-region and a confidence level or a metricof uncertainty is determined 54 for the region 34 or the sub-region. Thesub-regions each span from the inner contour 28 to the outer contour 26.Further, the sub-regions can at least partially be identified by theassociated user using the user interface. Additionally or alternatively,the sub-regions can at least partially be identified using an algorithm.For example, the band of uncertainty 32 can be broken into apredetermined number of sub-regions of equal area. The sub-regions canbe processed sequentially and/or in parallel.

With reference to FIG. 5, to delineate 54 the boundary in the region 34or the sub-region and determine 54 a confidence level or a metric ofuncertainty for the region 34 or the sub-region, a determination 56 ismade as to whether it is possible to determine the boundary of the OOI20 in the region 34 or the sub-region. The determination 56 can beperformed manually and/or automatically. As to the former, the manualdetermination can be made by the associated user through receipt of datafrom the user input device 30. For example, the associated user can viewthe received image 18 using the user interface to make thedetermination. The boundary can be manually determined with confidenceif the boundary points can be visually delineated in the received image18. As to the latter, an algorithm can be employed to assess whether itis possible to determine the boundary of the OOI 20 in the region 34 orthe sub-region with confidence. The boundary can be automaticallydetermined with confidence if the boundary points include a strengthexceeding that of other points in the filtered image.

If it is possible to manually and/or automatically determine theboundary of the OOI 20 in the region 34 or the sub-region withconfidence, the boundary is manually and/or automatically delineated 58in the received image 18. As to the former, the user interface can beemployed to allow the associated user to draw at least part of a contouraround the OOI 20 of the region or the sub-region. As to the latter, analgorithm can be employed. If it is not possible to manually and/orautomatically determine the boundary of the OOI 20 in the region 34 orthe sub-region with confidence, a determination 60 is made as to whethera stopping condition is met.

A stopping condition indicates that enhancement of the region 34 or thesub-region is of no value. The stopping condition can be, for example, apredetermined number of iterations, discussed below, a confidence levelof the boundary being less than a predetermined level, or an intensitygradient of the points in the region 34 or the sub-region being zero oruniform. Confidence can be assessed using a function of at least thenumber of iterations, discussed below. If the stopping condition isreached, the boundary is typically not clearly visible or a uniformdensity is obtained. To address this, the associated user can delineate56 the boundary of the OOI 20 in the region 34 or the sub-region usinganother image and, optionally, register the other image to the receivedimage 18. Additionally, or alternatively, the boundary of the OOI 20 inthe region 34 or the sub-region can be delineated 56 in the receivedimage 18 along the midline of the region 34 or the sub-region.

If the stopping condition is not reached, a filtering algorithm forenhancing edges is then iteratively run 62 in the region 34 or thesub-region for a predetermined number of iterations, such as fiveiterations, and the determination 56 is repeated. Typically, thefiltering algorithm is a stochastic scale space algorithm, but anyfiltering algorithm can be employed. The predetermined number ofiterations is suitably determined by the associated user and/or anadministrator of the segmentation device 16. Further, the predeterminednumber is the number of iterations the one determining the predeterminednumber deems to be sufficient to achieve a noticeable enhancement to theregion 34 or the sub-region.

FIG. 6 illustrates several images where the edge is strengthened to anextent that it can be subsequently delineated just by thresholding. FIG.6A shows the original image, FIG. 6B shows the original image after 50iterations of the filtering algorithm, FIG. 6C shows the original imageafter 100 iterations of the filtering algorithm, and FIG. 6D shows theoriginal image after 200 iterations of the filtering algorithm. Hence,FIG. 6 illustrates the progression of edge enhancement for an increasingnumber of iterations.

In order to speed up the method 50, the filtering of the region 34, thefiltering can be done only across the boundary (i.e., the directionperpendicular to the boundary direction). In that regard, the region 34of the band of uncertainty 32 can be divided into the plurality ofsub-regions, one for each point within the band of uncertainty 32(hereafter referred to as a point of uncertainty). The sub-region for apoint of uncertainty 36 is defined by all the points along an outer linesegment 38 and inner line segment 40, examples of which are illustratedin FIG. 7, of the point of uncertainty 36. The outer line segment 38 isdetermined by joining the point of uncertainty 36 with its projection onthe outer contour 26, and the inner line segment 40 is determined byjoining the point of uncertainty 36 with its projection on the innercontour 28. The projection of the point of uncertainty 36 on the outercontour 26 is the point on the outer contour 26 which is closest to thepoint of uncertainty 36, and the projection of the point of uncertainty36 on the inner contour 28 is the point on the inner contour 28 which isclosest to the point of uncertainty 36. As should be appreciated, theplurality of sub-regions includes overlapping sub-regions.

Referring back to FIG. 5, once the boundary of the OOI 20 is determinedfor the region 34 or the sub-region, a confidence level or a metric ofuncertainty is determined 64 for the region 34 or the sub-region. Theconfidence level and the metric of uncertainty are based on the extentof filtering needed to determine the boundary. Further, the metric ofuncertainty and the confidence level are inversely related. For example,as the number of filtering iterations increase, the uncertaintyincreases and the confidence level decreases. Hence, the confidencelevel can be determined from the metric of uncertainty and vice versa.

For example, uncertainty for the region 34 or the sub-region can bedetermined as follows. If the boundary of the region 34 or thesub-region was drawn with confidence without any filtering, anuncertainly value of 0 can be assigned to the region 34 or thesub-region. If the boundary was determined at the nth iteration, a valuebetween 0 and 100 can be chosen, depending on the number of iterations nand the strength of the boundary.

One choice for such a metric is:

Uncertainty(x)=100*exp(−∇F(x)/n),

where x is the number of iterations and F(x) is strength gradient of theregion 34 or the sub-region. Other choices are also possible, providedthey preserve continuity at the limit points (i.e., the metric should be0 when n=0 and 100 when the gradient F(x) is 0). If the midline of theregion 34 or the sub-region was employed, the region 34 or thesub-region can be assigned a maximum uncertainty value (e.g., 100).Confidence can then be defined as the additive inverse of Uncertaintywith respect to 100. In other words, confidence can be defined asfollows:

Confidence(x)=100−Uncertainty(x)

Once the boundary of the region 34 or all of the sub-regions isidentified, the segmentation is complete. The contour corresponding tothe identified boundary can then be displayed with the received image 18using the user interface. Further, the contour can be color codedaccording to confidence level and/or metric of uncertainty. For example,each boundary point can be assigned a color unique to the correspondingconfidence level or metric of uncertainty.

Using the boundary along with the corresponding confidence level and/ormetric of uncertainty, clinicians can diagnose a patient and determinethe best treatment options. Clinicians use the irregular and imprecisenature of the boundary as an important characteristic of tumors, whichdistinguishes them from benign lesions. For example, tumor boundaryirregularity can be used to distinguish between active tuberculosisnodules and malignant lesions. Confidence level and/or metric ofuncertainty can be employed to determine whether a boundary is irregularand imprecise. Further, the confidence of the final diagnosis can bedetermined based on confidence level and/or metric of uncertainty of thesegmentation. More conservative treatment options can be employed when,for example, the confidence in the final diagnosis is low.

Referring back to FIG. 1, the segmentation device 16 include at leastone processor 42 executing computer executable instructions on at leastone memory 44 thereof. The computer executable instructions carry outthe functionality of the segmentation device 16 and include thesegmentation application 22. Further, the segmentation device 16 caninclude a communication unit 46 and/or at least one system bus 48. Thecommunications unit 46 provides the processor 42 with an interface to atleast one communication network. The communications unit 46 can, forexample, be employed to communicate with the imaging modalities 12and/or the image memories 14. The system bus 48 allows the exchange ofdata between the display device 24, the user input device 30, theprocessor 42, the memory 44 and the communication unit 46.

As used herein, a memory includes one or more of a non-transientcomputer readable medium; a magnetic disk or other magnetic storagemedium; an optical disk or other optical storage medium; a random accessmemory (RAM), read-only memory (ROM), or other electronic memory deviceor chip or set of operatively interconnected chips; an Internet/Intranetserver from which the stored instructions may be retrieved via theInternet/Intranet or a local area network; or so forth. Further, as usedherein, a processor includes one or more of a microprocessor, amicrocontroller, a graphic processing unit (GPU), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and the like; a user input device includes one ormore of a mouse, a keyboard, a touch screen display, one or morebuttons, one or more switches, one or more toggles, and the like; adatabase includes one or more memories; and a display device includesone or more of a LCD display, an LED display, a plasma display, aprojection display, a touch screen display, and the like.

The invention has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be construed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

1. A system for quantification of uncertainty of contours, said system(10) comprising: at least one processor programmed to: receive an imageincluding an object of interest (OOI) (20); receive a band ofuncertainty delineating a region in the received image, the regionincluding the boundary of the OOI (20); delineate the boundary in theregion using iterative filtering of the region; and, determine at leastone metric of uncertainty of the delineation for the region.
 2. Thesystem according to claim 1, wherein the band of uncertainty includes aninner contour and an outer contour, the inner contour within the outercontour.
 3. The system according to claim 1, wherein the processor isfurther programmed to: display a contour representing the delineatedboundary of the OOI, the contour color coded according to metric ofuncertainty.
 4. The system according to claim 1, wherein the processoris further programmed to: for each of at least one of a plurality ofsub-regions defining the region: iteratively filter the sub-region untilthe boundary in the sub-region can be delineated with a confidence levelexceeding a predetermined level; and, delineate the boundary in thefiltered sub-region.
 5. The system according to claim 4, wherein theprocessor is further programmed to: for each of the at least one of theplurality of sub-regions: determine the metric of uncertainty of thedelineation for the sub-region, the metric of uncertainty based on thenumber of iterations.
 6. The system according to claim 4, wherein theplurality of sub-regions include a sub-region for each point ofuncertainty within the region, the sub-region defined by the points ofan inner line segment and an outer line segment of the point ofuncertainty (36), the inner line segment spanning from the point ofuncertainty to a projection of the point of uncertainty on an innercontour of the band of uncertainty, and the outer line segment spanningfrom the point of uncertainty to a projection of the point ofuncertainty on an outer contour of the band of uncertainty.
 7. Thesystem according to claim 6, wherein the projection of the point ofuncertainty on the inner contour of the band of uncertainty is a pointon the inner contour closest to the point of uncertainty and/or theprojection of the point of uncertainty on the outer contour of the bandof uncertainty is a point on the outer contour closest to the point ofuncertainty.
 8. The system according to claim 1, where the processor isfurther programmed to: for each of a plurality of sub-regions definingthe region: determine whether to filter the sub-region, thedetermination including at least one of: determining whether theboundary of the OOI can be delineated in the sub-region with aconfidence level exceeding a first predetermined level; and, determiningwhether a stopping condition is met, the stopping condition indicatingthe confidence level is less than a second predetermined level; inresponse to determining the the boundary of the OOI can be delineated inthe sub-region with the confidence level exceeding the firstpredetermined level or the stopping condition being met, delineate theboundary of the OOI in the sub-region; and, in response to determiningthe the boundary of the OOI cannot be delineated in the sub-region withthe confidence level exceeding the first predetermined level and thestopping condition not being met, iteratively filter the sub-region apredetermined number of times and repeat the determination as to whetherto filter the sub-region.
 9. The system according to claim 8, whereinthe stopping condition is the sub-region having a uniform density. 10.The system according to claim 8, where the processor is furtherprogrammed to: in response to the stopping condition being met,delineate the boundary of the OOI along the midline of the sub-region.11. The system according to claim 8, wherein the processor is furtherprogrammed to: in response to determining the the boundary of the OOIcan be delineated in the sub-region with a confidence level exceedingthe first predetermined level or the stopping condition being met,determine the metric of uncertainty of the delineation for thesub-region.
 12. A system for quantification of uncertainty of contours,said system (10) comprising: at least one processor programmed to:receive an image including an object of interest (OOI); receive a bandof uncertainty delineating a region in the received image, the regionincluding the boundary of the OOI; for each of a plurality ofsub-regions of the region: determine whether to filter the sub-region,the determination including at least one of: determining whether theboundary of the OOI can be delineated in the sub-region with aconfidence level exceeding a first predetermined level; and, determiningwhether a stopping condition is met, the stopping condition indicatingthe confidence level is less than a second predetermined level; inresponse to determining the the boundary of the OOI can be delineated inthe sub-region with the confidence level exceeding the firstpredetermined level or the stopping condition being met, delineate theboundary of the OOI in the sub-region; and, in response to determiningthe the boundary of the OOI cannot be delineated in the sub-region withthe confidence level exceeding the first predetermined level and thestopping condition not being met, iteratively filter the sub-region apredetermined number of times and repeat the determination as to whetherto filter the sub-region.
 13. The system according to claim 12, whereinthe processor is further programmed to: in response to determining thethe boundary of the OOI can be delineated in the sub-region with aconfidence level exceeding the first predetermined level or the stoppingcondition being met, determine the metric of uncertainty of thedelineation for the sub-region.